Search Results (1,115)

Background/Objectives. Efficient nucleic acid delivery into target cells remains a critical challenge in gene therapy. Due to its advantages in biocompatibility and safety, recent research has increasingly focused on non-viral gene delivery. Methods. A series of copolymers—synthesized by integrating thermally sensitive poly(N-isopropylacrylamide) (PNIPAm), hydrophilic poly(ethylene glycol) (PEG) grafts, and a polycationic poly(L-lysine) (PLL) block of varying lengths ((PNIPAm)₇₇-graft-(PEG)₉-block-(PLL)_z, z = 10–65)—were investigated. Plasmid DNA complexation with the copolymers was achieved through temperature-modulated methods. The resulting polyplexes were characterized by evaluating complex strength, particle size, zeta potential, plasmid DNA loading capacity, resistance to anionic stress, stability in serum, and lysosomal membrane destabilization assay. The copolymers’ potential for plasmid DNA delivery was assessed through cytotoxicity and transfection studies in cancer cell lines. Results. Across all complexation methods, the copolymers effectively condensed plasmid DNA into stable polyplexes. Particle sizes (60–90 nm) ranged with no apparent correlation to copolymer type, complexation method, or N/P ratio, whereas zeta potentials (+10–+20 mV) and resistance to polyanionic stress were dependent on the PLL length and N/P ratio. Cytotoxicity analysis revealed a direct correlation between PLL chain length and cell viability, with all copolymers demonstrating minimal cytotoxicity at concentrations required for efficient transfection. PNL-20 ((PNIPAm)₇₇-graft-(PEG)₉-block-(PLL)₂₀) exhibited the highest transfection efficiency among the tested formulations while maintaining low cytotoxicity. Conclusions. The study highlights the promising potential of (PNIPAm)₇₇-graft-(PEG)₉-block-(PLL)_z copolymers for effective plasmid DNA delivery to cancer cells. It reveals the importance of attaining the right balance between polyplex tightness and plasmid release to achieve improved biocompatibility and transfection efficiency. Full article

Refining the land use structure can boost land utilization efficiency and curtail regional carbon emissions. Nevertheless, prior research has predominantly concentrated on static linear planning analysis. It has failed to account for how future dynamic alterations in driving factors (such as GDP and population) affect simulation outcomes and how the land use spatial configuration impacts the attainment of the carbon-neutrality goal. In this research, 1 km spatial resolution LULC products were employed to meticulously simulate multiple land use scenarios across China at the national level from 2030 to 2060. This was performed by taking into account the dynamic changes in driving factors. Subsequently, an analysis was carried out on the low-carbon land use spatial structure required to reach the carbon-neutrality target. The findings are as follows: (1) When employing the PLUS (Patch—based Land Use Simulation) model to conduct simulations of various land use scenarios in China by taking into account the dynamic alterations in driving factors, a high degree of precision was attained across diverse scenarios. The sustainable development scenario demonstrated the best performance, with kappa, OA, and FoM values of 0.9101, 93.15%, and 0.3895, respectively. This implies that the simulation approach based on dynamic factors is highly suitable for national-scale applications. (2) The simulation accuracy of the PLUS and GeoSOS-FLUS (Systems for Geographical Modeling and Optimization, Simulation of Future Land Utilization) models was validated for six scenarios by extrapolating the trends of influencing factors. Moreover, a set of scenarios was added to each model as a control group without extrapolation. The present research demonstrated that projecting the trends of factors having an impact notably improved the simulation precision of both the PLUS and GeoSOS-FLUS models. When contrasted with the GeoSOS-FLUS model, the PLUS model attained superior simulation accuracy across all six scenarios. The highest precision indicators were observed in the sustainable development scenario, with kappa, OA, and FoM values reaching 0.9101, 93.15%, and 0.3895, respectively. The precise simulation method of the PLUS model, which considers the dynamic changes in influencing factors, is highly applicable at the national scale. (3) Under the sustainable development scenario, it is anticipated that China’s land use carbon emissions will reach their peak in 2030 and achieve the carbon-neutrality target by 2060. Net carbon emissions are expected to decline by 14.36% compared to the 2020 levels. From the perspective of dynamic changes in influencing factors, the PLUS model was used to accurately simulate China’s future land use. Based on these simulations, multi-scenario predictions of future carbon emissions were made, and the results uncover the spatiotemporal evolution characteristics of China’s carbon emissions. This study aims to offer a solid scientific basis for policy-making related to China’s low-carbon economy and high-quality development. It also intends to present Chinese solutions and key paths for achieving carbon peak and carbon neutrality. Full article

Urban health planning requires a multi-scalar understanding of the territory, capable of capturing socio-economic inequalities and health needs at the local level. In the case of Rome, current administrative subdivisions—Urban Zones (Zone Urbanistiche)—are too large and internally heterogeneous to serve as effective units for equitable health planning. This study presents a methodology for the territorial redefinition of Rome’s Municipality III, aimed at supporting healthcare planning through an integrated analysis of census sections. These were grouped using a combination of census-based socio-demographic indicators (educational attainment, employment status, single-person households) and real estate values (OMI data), alongside administrative and road network data. The resulting territorial units—21 newly defined Mesoareas—are smaller than Urban Zones but larger than individual census sections and correspond to socio-territorially homogeneous neighborhoods; this structure enables a more nuanced spatial understanding of health-related inequalities. The proposed model is replicable, adaptable to other urban contexts, and offers a solid analytical basis for more equitable and targeted health planning, as well as for broader urban policy interventions aimed at promoting spatial justice. Full article

Background/Objectives: Quality of life (QOL) is a multifaceted concept involving a variety of factors which define the overall well-being of individuals. Food security, which implies a resilient food system, is one factor that is central to the calculus of the QOL status of a community considering that food is a staple of life. Advancing food security as a strategy for attaining sustained improvement in community QOL hinges on recognizing that food security is embedded in a matrix of other factors that work with it to generate the QOL the community experiences. The lived experience of the community defines the community’s QOL value matrix and the relative position of food security in that value matrix. Our thesis is that the role of food security in the lived experience of low-income communities depends on the position food security is accorded relative to other factors in the QOL value matrix of the community. Methods: This study employed a multimethod approach to define the QOL value matrix of low-income Guilford County residents, identifying the relative position of the value components and demographic segments based on priority ranking. First, an in-depth interview was conducted and then a telephone survey (280 sample) was used for collecting data. The ISAC Analysis Procedure and Best–Worst Scaling methods were used to identify and rank components of the QOL value matrix in terms of their relative impact on QOL. Results: The analysis revealed that spiritual well-being is the most important contributor to QOL, with a weight of 0.23, followed by access to health services (0.21) and economic opportunities (0.16), while food security has a moderate impact with 0.07. Conclusions: These findings emphasize the need for targeted policy interventions that consider the specific needs of different demographic segments to effectively improve QOL and inform the design of resilient food systems that reflect the lived experiences of low-income communities. Food security policies must be integrated with broader quality of life interventions, particularly for unemployed, low-educated, and single individuals, to ensure that a resilient food system effectively reduces inequities and address community-specific vulnerabilities. Full article

This paper analyzes the historical genealogy of conspiracy theories about a global supergovernment by focusing on one period of American history in which it attained particular visibility. The formation of the United Nations in 1945 and the onset of the Cold War galvanized speculation on the political margins that a shadowy, malevolent international government was seeking world domination by targeting the United States and its political culture. At the same time, mainstream United States foreign policy was marked by a desire to both reshape international institutions to resist Soviet influence while also avoiding any domestic changes that might result from international engagement. This paper suggests that conspiracy theory functioned as a mechanism resolving the vicious circle occasioned by these competing foreign policy priorities. Through a narrative analysis of conspiratorial sentiments in North Dakota Representative Usher L. Burdick’s warnings about the United Nations as a threat to American liberty and sovereignty, this article highlights the continuities between mainstream American exceptionalism and conspiratorial ideas. Full article

Background/Objectives: Current dosing recommendations for piperacillin/tazobactam suggest adjustments only for patients with creatinine clearance (CrCl) below 40 mL/min, potentially neglecting the variability in drug exposure among patients with a CrCl greater than 40 mL/min. This study aimed to develop a population pharmacokinetic (PK) model for piperacillin/tazobactam and explore optimal dosage regimens tailored by renal function and pathogen susceptibility. Methods: Twelve healthy adults received a single intravenous dose of piperacillin/tazobactam (4 g/0.5 g). Population PK models were developed using nonlinear mixed-effects modeling. Monte Carlo simulations were conducted to identify optimal dosing regimens across various renal functions and MIC levels, guided by pharmacodynamic targets defined as the percentage of time that free drug concentrations exceed the minimum inhibitory concentration (fT_>MIC). Results: PK profiles of both drugs were best described by two-compartment models. Estimated glomerular filtration rate (eGFR) adjusted by body surface area and body weight were identified as significant covariates influencing drug clearance and peripheral volume of distribution. Simulations showed that the standard dosing regimen (4/0.5 g q6h with 30 min infusion) achieved a 90% probability of target attainment (PTA) for 50%fT_>MIC at MIC values up to 4 mg/L in patients with normal renal function. However, this regimen often did not achieve a 90% PTA for stringent targets (100%fT_>MIC, 100%fT_>4MIC) or higher MICs, particularly in patients with eGFR ≥ 130 mL/min. Conclusions: These findings suggest current dosing regimens may be inadequate and highlight the potential of alternative strategies, such as extended or continuous infusion, which warrant further investigation in clinical populations to optimize therapeutic outcomes. Full article

Although intracranial hypertension (ICH) has traditionally been framed as simply a numerical escalation of intracranial pressure (ICP) and usually dealt with in its clinical form and not in terms of its complex underlying pathophysiology, an emerging body of evidence indicates that ICH is not simply an elevated ICP process but a complex process of molecular dysregulation, glymphatic dysfunction, and neurovascular insufficiency. Our aim in this paper is to provide a complete synthesis of all the new thinking that is occurring in this space, primarily on the intersection of glymphatic dysfunction and cerebral vein physiology. The aspiration is to review how glymphatic dysfunction, largely secondary to aquaporin-4 (AQP4) dysfunction, can lead to delayed cerebrospinal fluid (CSF) clearance and thus the accumulation of extravascular fluid resulting in elevated ICP. A range of other factors such as oxidative stress, endothelin-1, and neuroinflammation seem to significantly impair cerebral autoregulation, making ICH challenging to manage. Combining recent studies, we intend to provide a revised conceptualization of ICH that recognizes the nuance and complexity of ICH that is understated by previous models. We wish to also address novel diagnostics aimed at better capturing the dynamic nature of ICH. Recent advances in non-invasive imaging (i.e., 4D flow MRI and dynamic contrast-enhanced MRI; DCE-MRI) allow for better visualization of dynamic changes to the glymphatic and cerebral blood flow (CBF) system. Finally, wearable ICP monitors and AI-assisted diagnostics will create opportunities for these continuous and real-time assessments, especially in limited resource settings. Our goal is to provide examples of opportunities that exist that might augment early recognition and improve personalized care while ensuring we realize practical challenges and limitations. We also consider what may be therapeutically possible now and in the future. Therapeutic opportunities discussed include CRISPR-based gene editing aimed at restoring AQP4 function, nano-robotics aimed at drug targeting, and bioelectronic devices purposed for ICP modulation. Certainly, these proposals are innovative in nature but will require ethically responsible confirmation of long-term safety and availability, particularly to low- and middle-income countries (LMICs), where the burdens of secondary ICH remain preeminent. Throughout the review, we will be restrained to a balanced pursuit of innovative ideas and ethical considerations to attain global health equity. It is not our intent to provide unequivocal answers, but instead to encourage informed discussions at the intersections of research, clinical practice, and the public health field. We hope this review may stimulate further discussion about ICH and highlight research opportunities to conduct translational research in modern neuroscience with real, approachable, and patient-centered care. Full article

Aircraft, as electromagnetically complex targets, have radar cross-sections (RCSs) that are influenced by various factors, with the inlet duct being a critical component that often serves as a primary source of electromagnetic scattering, significantly impacting the scattering characteristics. In light of the conflict between aerodynamic performance and electromagnetic characteristics in the design of aircraft engine inlet grilles, this paper proposes a metasurface radar-absorbing inlet grille (RIG) solution based on a NACA symmetric airfoil. The RIG adopts a sandwich structure consisting of a polyethylene terephthalate (PET) dielectric substrate, a copper zigzag metal strip array, and an indium tin oxide (ITO) resistive film. By leveraging the principles of surface plasmon polaritons, electromagnetic wave absorption can be achieved. To enhance the design efficiency, a multi-objective Bayesian optimization framework driven by the expected hypervolume improvement (EHVI) is constructed. The results show that, compared with a conventional rectangular cross-section grille, an airfoil-shaped grille under the same constraints will reduce both aerodynamic losses and the absorption bandwidth. After 100-step EHVI–Bayesian optimization, the optimized balanced model attains a 57.79% reduction in aerodynamic loss relative to the rectangular-shaped grille, while its absorption bandwidth increases by 111.99%. The RCS exhibits a reduction of over 8.77 dBsm in the high-frequency band. These results confirm that the proposed optimization design process can effectively balance the conflict between aerodynamic performance and stealth performance for RIGs, reducing the signal strength of aircraft engine inlets. Full article

Achieving targeted nanoparticle (NP) size and concentration combinations in Pulsed Laser Ablation in Liquid (PLAL) remains a challenge due to the highly nonlinear relationships between laser processing parameters and NP properties. Despite the promise of PLAL as a surfactant-free, scalable synthesis method, its industrial adoption is hindered by empirical trial-and-error approaches and the lack of predictive tools. The current literature offers limited application of machine learning (ML), particularly recommender systems, in PLAL optimization and automation. This study addresses this gap by introducing a ML-based recommender system trained on a 3 × 3 design of experiments with three replicates covering variables, such as fluence (1.83–1.91 J/cm²), ablation time (5–25 min), and laser scan speed (3000–3500 mm/s), in producing magnesium nanoparticles from powders. Multiple ML models were evaluated, including K-Nearest Neighbors (KNN), Extreme Gradient Boosting (XGBoost), Random Forest, and Decision trees. The DT model achieved the best performance for predicting the NP size with a mean percentage error (MPE) of 10%. The XGBoost model was optimal for predicting the NP concentration attaining a competitive MPE of 2%. KNN and Cosine similarity recommender systems were developed based on a database generated by the ML predictions. This intelligent, data-driven framework demonstrates the potential of ML-guided PLAL for scalable, precise NP fabrication in industrial applications. Full article

This study investigates the implementation and impact of the Guided Personalised Learning (GPL) model, a structured pedagogical framework designed to operationalise personalised and student-centred learning in STEM higher education. The GPL model integrates three interconnected components: a three-dimensional knowledge and skill grid, Interactive Learning Progress Assessments (ILPA), and an adaptive learning resource pool. These components were embedded into two undergraduate engineering modules, Network Engineering and Software Engineering, at a UK university. A mixed-method evaluation, centred on student attainment data across two academic years, revealed statistically significant improvements among students who engaged with GPL, particularly those who completed ILPA activities. Participation was associated with higher mean grades, increased proportions of high achievers, and reduced failure rates. These findings demonstrate the GPL model’s effectiveness in supporting learner autonomy, formative assessment, and targeted feedback, while offering a scalable strategy for integrating personalised learning into mainstream STEM curricula. Full article

In this work, the development of Al-based metal matrix composites (MMCs) is achieved using hybrid SiC and ZrO₂ reinforcement particles for automotive applications. Powder metallurgy (PM) is employed with various combinations of important process parameters for the fabrication of MMCs. MMCs were characterized using scanning electron microscopy (SEM), X-ray diffractometry (XRD), and a microhardness study. All XRD graphs adequately exhibit Al, SiC, and ZrO₂ peaks, indicating that the hybrid MMC products were satisfactorily fabricated with appropriate mixing and sintering at all the considered fabrication conditions. Also, no impurity peaks were observed, confirming high composite purity. MMC products in all the XRD patterns, suitable for the desired applications. According to the SEM investigation, SiC and ZrO₂ reinforcement components are uniformly scattered throughout Al matrix in all produced MMC products. The occurrence of Al, Si, C, Zr, and O in EDS spectra demonstrates the effectiveness of composite ball milling and sintering under all manufacturing conditions. Moreover, an increase in interfacial bonding of fabricated composites at a higher sintering temperature indicated improved physical properties of the developed MMCs. The highest microhardness value is 86.6 HVN amid all the fabricated composites at 7% silica, 14% zirconium dioxide, 500° sintering temperature, 90 min sintering time, and 60 min milling time. An integrated Particle Swarm Optimization–Support Vector Machine (PSO-SVM) model was developed to predict microhardness based on the input parameters. The model demonstrated strong predictive performance, as evidenced by low values of various statistical metrics for both training and testing datasets, highlighting the PSO-SVM model’s robustness and generalization capability. Specifically, the model achieved a coefficient of determination of 0.995 and a root mean square error of 0.920 on the training set, while on the testing set, it attained a coefficient of determination of 0.982 and a root mean square error of 1.557. These results underscore the potential of the PSO-SVM framework, which can be effectively leveraged to optimize process parameters for achieving targeted microhardness levels for the developed Al-SiC-ZrO₂ Composites. Full article

As digital transformation accelerates across Europe, cloud computing plays an increasingly central role in modernizing public services and private enterprises. Yet adoption rates vary markedly among EU member states, reflecting deeper structural differences in digital capacity. This study employs explainable machine learning to uncover the drivers of national cloud adoption across 27 EU countries using harmonized panel datasets spanning 2014–2021 and 2014–2024. A methodological pipeline combining Random Forests (RF), XGBoost, Support Vector Machines (SVM), and Elastic Net regression is implemented, with model tuning conducted via nested cross-validation. Among individual models, Elastic Net and SVM delivered superior predictive performance, while a stacked ensemble achieved the best overall accuracy (MAE = 0.214, R² = 0.948). The most interpretable model, a standardized RF with country fixed effects, attained MAE = 0.321, and R² = 0.864, making it well-suited for policy analysis. Variable importance analysis reveals that the density of ICT specialists is the strongest predictor of adoption, followed by broadband access and higher education. Fixed-effect modeling confirms significant national heterogeneity, with countries like Finland and Luxembourg consistently leading adoption, while Bulgaria and Romania exhibit structural barriers. Partial dependence and SHAP analyses reveal nonlinear complementarities between digital skills and infrastructure. A hierarchical clustering of countries reveals three distinct digital maturity profiles, offering tailored policy pathways. These results directly support the EU Digital Decade’s strategic targets and provide actionable insights for advancing inclusive and resilient digital transformation across the Union. Full article

Many plant image-based studies primarily use datasets featuring either a single plant, a plant with only one leaf, or images containing only plants and leaves without any background. However, in real-world scenarios, a substantial portion of acquired images consists of blurred plants or extensive backgrounds rather than high-resolution details of the target plants. In such cases, classification models struggle to identify relevant areas for classification, leading to insufficient information and reduced classification performance. Moreover, the presence of moisture, water droplets, dust, or partially damaged leaves further degrades classification accuracy. To address these challenges and enhance classification performance, this study introduces a plant image segmentation (Pl-ImS) model for segmentation and a plant image classification (Pl-ImC) model for classification. The proposed models were evaluated using a self-collected dataset of 21,760 plant images captured under real field conditions in South Korea, incorporating various environmental factors such as moisture, water droplets, dust, and partial leaf loss. The segmentation method achieved a dice score (DS) of 89.90% and an intersection over union (IoU) of 81.82%, while the classification method attained an F1-score of 95.97%, surpassing state-of-the-art methods. Full article

Cotton leaf diseases can lead to substantial yield losses and economic burdens. Traditional detection methods are challenged by low accuracy and high labor costs. This research presents the ACURS-YOLO network, an advanced cotton leaf disease detection architecture developed on the foundation of YOLOv11. By integrating a medical image segmentation model, it effectively tackles challenges including complex background interference, the missed detection of small targets, and restricted generalization ability. Specifically, the U-Net v2 module is embedded in the backbone network to boost the multi-scale feature extraction performance in YOLOv11. Meanwhile, the CBAM attention mechanism is integrated to emphasize critical disease-related features. To lower the computational complexity, the SPPF module is substituted with SimSPPF. The C3k2_RCM module is appended for long–range context modeling, and the ARelu activation function is employed to alleviate the vanishing gradient problem. A database comprising 3000 images covering six types of cotton leaf diseases was constructed, and data augmentation techniques were applied. The experimental results show that ACURS-YOLO attains impressive performance indicators, encompassing a mAP_0.5 value of 94.6%, a mAP_0.5:0.95 value of 83.4%, 95.5% accuracy, 89.3% recall, an F1 score of 92.3%, and a frame rate of 148 frames per second. It outperforms YOLOv11 and other conventional models with regard to both detection precision and overall functionality. Ablation tests additionally validate the efficacy of each component, affirming the framework’s advantage in addressing complex detection environments. This framework provides an efficient solution for the automated monitoring of cotton leaf diseases, advancing the development of smart sensors through improved detection accuracy and practical applicability. Full article

Background and Objectives: Excessive social media use is repeatedly linked to negative body image outcomes, yet its association with muscle dysmorphia, especially in athletic youth, remains underexplored. We investigated how social media exposure, comparison behavior, and platform engagement relate to muscle dysmorphia symptomatology in young German athletes. Materials and Methods: An anonymous, web-based cross-sectional survey was conducted (July–October 2024) of 540 individuals (45% female; mean age = 24.6 ± 5.3 years; 79% ≥ 3 h sport/week) recruited via Instagram. The questionnaire comprised demographics, sport type, detailed social media usage metrics, and the validated German Muscle Dysmorphic Disorder Inventory (MDDI-1, 15 items). Correlations (Spearman’s ρ, Kendall’s τ) were calculated; multivariate importance was probed with classification-and-regression trees and CatBoost gradient boosting, interpreted via SHAP values. Results: Median daily social media time was 76 min (IQR 55–110). Participants who spent ≥ 60 min per day on social media showed higher MDDI scores (mean 38 ± 7 vs. 35 ± 6; p = 0.010). The strongest bivariate link emerged between perceived social media-induced body dissatisfaction and felt pressure to attain a specific body composition (Spearman ρ = 0.748, Kendall τ = 0.672, p < 0.001). A CatBoost gradient-boosting model out-performed linear regression in predicting elevated MDDI. The three most influential features (via SHAP values) were daily social media time, frequency of comparison with fitness influencers, and frequency of “likes”-seeking behavior. Conclusions: Intensive social media exposure substantially heightens muscle dysmorphia risk in young German athletes. Machine-learning interpretation corroborates time on social media and influencer comparisons as primary drivers. Interventions should combine social media literacy training with sport-specific psychoeducation to mitigate maladaptive comparison cycles and prevent downstream eating disorder pathology. Longitudinal research is warranted to clarify causal pathways and to test targeted digital media interventions. Full article